
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "78"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 22 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 22 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break


for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459998	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	26.199958	0.630285	-0.350275	1.155564	1.391443	1.098184	0.631338	0.379703	0.4171	0.6053	0.3675	nan	nan
2459997	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	28.348047	0.651597	-0.269535	1.279756	2.122966	1.059847	-0.316610	0.940472	0.4427	0.6299	0.3741	nan	nan
2459996	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	28.955570	1.277913	-0.309347	1.485922	2.106773	1.306237	5.620044	0.342679	0.4340	0.6215	0.3818	nan	nan
2459995	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	31.102708	0.840537	-0.468175	1.451715	1.551493	0.938631	3.089761	0.125543	0.4424	0.6260	0.3706	nan	nan
2459994	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	32.158663	0.510469	-0.352954	1.251250	1.855335	0.708492	0.801225	-0.005638	0.4343	0.6198	0.3675	nan	nan
2459993	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	37.398001	1.167525	-0.271492	1.480329	2.692613	0.763709	1.775302	-0.165422	0.4090	0.6303	0.3921	nan	nan
2459991	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	38.742808	0.775794	-0.302513	1.484790	2.303732	0.939636	2.741577	-0.001108	0.4248	0.6131	0.3777	nan	nan
2459990	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	32.315102	0.675036	-0.301208	1.567483	2.253765	1.131056	2.111224	0.383468	0.4215	0.6160	0.3779	nan	nan
2459989	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	32.505523	0.594529	-0.162183	1.360455	1.870929	0.521112	1.204717	-0.122559	0.4202	0.6163	0.3800	nan	nan
2459988	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	38.328967	0.927564	-0.374318	1.618267	3.082994	1.709353	2.943008	0.642266	0.4148	0.6049	0.3664	nan	nan
2459987	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	30.644557	0.647038	-0.377908	1.278273	1.554610	0.563786	-0.179595	0.287633	0.4364	0.6248	0.3681	nan	nan
2459986	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	37.231917	1.013579	-0.424285	1.524819	2.259761	0.146277	2.585436	1.645165	0.4602	0.6398	0.3358	nan	nan
2459985	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	34.731824	0.982230	-0.457262	1.236438	1.588198	0.238647	3.642595	0.634328	0.4312	0.6203	0.3742	nan	nan
2459984	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	31.843441	0.795673	-0.391064	1.313570	3.404819	1.774430	2.104639	1.327276	0.4523	0.6309	0.3512	nan	nan
2459983	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	32.841434	0.017107	-0.380738	1.174815	2.457828	0.372592	2.428701	0.561796	0.4802	0.6559	0.3154	nan	nan
2459982	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	15.949253	-0.100663	-0.826031	0.430399	-0.035171	-0.923640	0.071474	-0.516982	0.5253	0.6753	0.2987	nan	nan
2459981	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	31.141957	-0.235663	-0.381827	1.374338	2.755279	0.301092	3.061327	1.239264	0.4245	0.6160	0.3738	nan	nan
2459980	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	28.294977	-0.348593	-0.590671	0.778548	2.256429	-0.329769	1.414681	0.229916	0.4916	0.6567	0.3180	nan	nan
2459979	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	32.362634	-0.488438	-0.516431	0.778595	2.137016	-0.525827	1.397140	0.721322	0.4196	0.6146	0.3758	nan	nan
2459978	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	32.415981	-0.367226	-0.458810	1.021382	2.316053	0.218761	2.198807	1.975215	0.4145	0.6091	0.3805	nan	nan
2459977	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	31.547309	-0.118651	-0.496014	0.766587	3.697786	0.132005	3.775022	2.928838	0.3992	0.5786	0.3431	nan	nan
2459976	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	31.884495	-0.242156	-0.515729	0.953585	1.893809	-0.041402	0.628457	0.399104	0.4292	0.6222	0.3766	nan	nan

Antenna Season Report Notebook¶

Antenna 78 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 78, Node N06:

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 78: 2459998

Antenna 78: 2459997

Antenna 78: 2459996

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Antenna 78: 2459976

Antenna Season Report Notebook¶

Antenna 78 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 78, Node N06:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 78: 2459998

Antenna 78: 2459997

Antenna 78: 2459996

Antenna 78: 2459995

Antenna 78: 2459994

Antenna 78: 2459993

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Antenna 78: 2459980

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Antenna 78: 2459978

Antenna 78: 2459977

Antenna 78: 2459976

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶